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2024 Projects: Drone and Satelite Imagery for Yield Predictions

How Best to Remotely Senced Imagery for Management of Corn, Soybean, and Sorghum?

Precision agriculture is moving toward the implementation of technology-driven farm management packages that also help with making better decisions about crop fertility management (decision agriculture). Of all nutrients essential for crop growth, N is most difficult to manage due to varying crop N demands throughout the season and inability to accurately predict N supply. Use of drone-obtained NDVI images has the promise to aid in quick and accurate decision making for better N management of field crops like corn and forage sorghum that have large and well-timed N needs but requires accurate estimation of (1) end-of-season yield; (2) soil N supply; and (3) crop N needs. Our overall objective here is to evaluate use of drone-collected images as tools for predicting yield and N needs for both crops.

If you are interested in participating, contact Quirine Ketterings (qmk2@cornell.edu or 607-255-3061). You can also write to: Quirine Ketterings, Nutrient Management Spear Program, Department of Animal Science, Cornell University, 323 Morrison Hall, Ithaca NY 14853.

Goals

    Our goals are to evaluate use of drone collected images for yield predictions of N responsiveness and needs for corn and forage sorghum as impacted by timing of flight (in the day and over the growing season).

Funding Sources

New York Farm Viability Institute, Federal Formula Funds, DuPont-Pioneer

Additional Resources

Farmer Impact Stories

Fact Sheets

Extension Articles

  • Srinivasagan, S.N., J.C. Ramos Tanchez, M. Marcaida III, S. Sunoj, K. Workman, and Q.M. Ketterings (2023). Single-Strip Spatial Evaluation Approach for Easier, More Meaningful On-Farm Research. Crops and Soils 56: 13-17.
  • Srinivasagan, S., M. Marcaida III, S. Sunoj, and Q.M. Ketterings (2023). Technology Makes On-farm Research Easier: Single-strip Spatial Evaluation Approach. Progressive Dairy; The Manager; March 2023.
  • S. Sunoj, J. Cho, J. Guinness, J. van Aardt, K.J. Czymmek, and Q.M. Ketterings (2022). Corn Grain Yield Estimation with Drones - Timing is Key! What's Cropping Up? 32(1).

Journal Articles

  • Sunoj, S., B. Yeh, M. Marcaida, L. Longchamps, J. van Aardt, and Q.M. Ketterings (2023). Maize grain and silage yield prediction of commercial fields using high-resolution UAS imagery. Biosystems Engineering 235: 137-149; https://doi.org/10.1016/j.biosystemseng.2023.09.010
  • Burglewski, N.M., Q.M. Ketterings, S. Sunoj, and J. van Aardt (2023). A comparison of traditional and machine learning corn yield models using hyperspectral UAS and Landsat imagery, Proc. SPIE 12519, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX, 125190B; https://doi.org/10.1117/12.2663715
  • Liao, Z., Dai, Y, Pei, S., Ketterings, Q.M., Lu, J., Zhang, F., Li. Z. (2022). A two-layer model for improving wheat canopy nitrogen content estimation from unmanned aerial vehicle multispectral imagery. Journal of Integrative Agriculture. Pre-proof: https://doi.org/10.1016/j.jia.2023.02.022
  • Sunoj, S., J. Cho, J. Guinness, J. van Aardt, K.J. Czymmek, and Q.M. Ketterings (2021). Corn grain yield prediction and mapping from unmanned aerial system (UAS) multispectral imagery. Remote Sensing. doi: 10.3390/rs13193948.
  • Maresma, A., L. Chamberlain, A. Tagarakis, T. Kharel, G. Godwin, K.J. Czymmek, E. Shields, and Q.M. Ketterings (2020). Accuracy of NDVI-derived corn yield predictions is impacted by time of sensing. Computers and Electronics in Agriculture 169: 105236. https://doi.org/10.1016/j.compag.2020.105236.